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home / docs / papers / cited-by / Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 4

Who cites «Are NLP Models really able to Solve Simple Math Word Problems?» · page 4

592 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

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  1. Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models — 2023 · arXiv:2304.09842
  2. Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  3. Contrastive Chain-of-Thought Prompting — 2023 · arXiv:2311.09277
  4. Contrastive Decoding Improves Reasoning in Large Language Models — 2023 · arXiv:2309.09117
  5. Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration — 2023 · arXiv:2310.00280
  6. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning — 2023 · arXiv:2310.02954
  7. Defining a New NLP Playground — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  8. Democratizing Reasoning Ability: Tailored Learning from Large Language Model — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  9. Design of Chain-of-Thought in Math Problem Solving — 2023 · arXiv:2309.11054
  10. DialCoT Meets PPO: Decomposing and Exploring Reasoning Paths in Smaller Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  11. Distilling Reasoning Capabilities into Smaller Language Models — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  12. Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  13. DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks — 2023 · arXiv:2309.17167
  14. Dynamic Voting for Efficient Reasoning in Large Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  15. Evaluating Large Language Models: A Comprehensive Survey — 2023 · arXiv:2310.19736
  16. Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  17. Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  18. Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  19. Expression Syntax Information Bottleneck for Math Word Problems — 2023 · arXiv:2310.15664
  20. FERMAT: An Alternative to Accuracy for Numerical Reasoning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  21. Faithful Chain-of-Thought Reasoning — 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
  22. Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems — 2023 · arXiv:2310.01991
  23. Foundation Models for Decision Making: Problems, Methods, and Opportunities — 2023 · arXiv:2303.04129
  24. From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning — 2023 · arXiv:2304.07995
  25. GRACE: Discriminator-Guided Chain-of-Thought Reasoning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  26. Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models — 2023 · arXiv:2310.06692
  27. Getting MoRE out of Mixture of Language Model Reasoning Experts — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  28. Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT — 2023 · arXiv:2304.11116
  29. GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approach — 2023 · arXiv:2308.09267
  30. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond — 2023 · arXiv:2304.13712
  31. How Many Demonstrations Do You Need for In-context Learning? — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  32. Improving Mathematics Tutoring With A Code Scratchpad — 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
  33. Is ChatGPT a General-Purpose Natural Language Processing Task Solver? — 2023 · arXiv:2302.06476
  34. It Ain''t Over: A Multi-aspect Diverse Math Word Problem Dataset — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  35. KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  36. LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  37. Language Models can Solve Computer Tasks — 2023 · arXiv:2303.17491
  38. Large Language Models Can Be Easily Distracted by Irrelevant Context — 2023 · arXiv:2302.00093
  39. Large Language Models Can Self-Improve — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  40. Large Language Models are Better Reasoners with Self-Verification — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  41. Learning From Mistakes Makes LLM Better Reasoner — 2023 · arXiv:2310.20689
  42. Learning Multi-Step Reasoning by Solving Arithmetic Tasks — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
  43. Learning by Analogy: Diverse Questions Generation in Math Word Problem — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  44. Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  45. Let''s Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  46. Leveraging Training Data in Few-Shot Prompting for Numerical Reasoning — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  47. MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning — 2023 · arXiv:2309.05653
  48. Making Language Models Better Reasoners with Step-Aware Verifier — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  49. Making Presentation Math Computable: A Context-Sensitive Approach for Translating LaTeX to Computer Algebra Systems — 2023 · monograph
  50. Math Word Problem Solving by Generating Linguistic Variants of Problem Statements — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
  51. MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning — 2023 · arXiv:2310.03731
  52. MathPile: A Billion-Token-Scale Pretraining Corpus for Math — 2023 · arXiv:2312.17120
  53. Mathematics, word problems, common sense, and artificial intelligence — 2023 · arXiv:2301.09723
  54. Mixed Distillation Helps Smaller Language Model Better Reasoning — 2023 · arXiv:2312.10730
  55. Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models — 2023 · arXiv:2305.14705
  56. NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  57. Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future — 2023 · arXiv:2309.15402
  58. Neural Machine Translation for Mathematical Formulae — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  59. Non-Autoregressive Math Word Problem Solver with Unified Tree Structure — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  60. Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  61. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  62. Progressive-Hint Prompting Improves Reasoning in Large Language Models — 2023 · arXiv:2304.09797
  63. Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution — 2023 · arXiv:2309.16797
  64. PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  65. R$^3$ Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  66. REFINER: Reasoning Feedback on Intermediate Representations — 2023 · arXiv:2304.01904
  67. Reasoning in Large Language Models Through Symbolic Math Word Problems — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  68. Retrieving Multimodal Information for Augmented Generation: A Survey — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  69. Scaling Relationship on Learning Mathematical Reasoning with Large Language Models — 2023 · arXiv:2308.01825
  70. Self-Evaluation Guided Beam Search for Reasoning — 2023 · arXiv:2305.00633
  71. Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  72. SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data — 2023 · Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
  73. TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  74. Tab-CoT: Zero-shot Tabular Chain of Thought — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  75. Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems — 2023 · arXiv:2308.05713
  76. The Flan Collection: Designing Data and Methods for Effective Instruction Tuning — 2023 · arXiv:2301.13688
  77. TheoremQA: A Theorem-driven Question Answering Dataset — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  78. Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  79. TinyGSM: achieving >80% on GSM8k with small language models — 2023 · arXiv:2312.09241
  80. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving — 2023 · arXiv:2309.17452
  81. Toolformer: Language Models Can Teach Themselves to Use Tools — 2023 · arXiv:2302.04761
  82. Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration — 2023 · arXiv:2311.08152
  83. Towards Reasoning in Large Language Models: A Survey — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  84. UniMath: A Foundational and Multimodal Mathematical Reasoner — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
  85. Unleashing the potential of prompt engineering for large language models — 2023 · arXiv:2310.14735 · Patterns 6(6) 101260 (2025)
  86. What Makes Chain-of-Thought Prompting Effective? A Counterfactual Study — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
  87. World Models for Math Story Problems — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
  88. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language Models — 2023 · arXiv:2311.15930
  89. ArMATH: a Dataset for Solving Arabic Math Word Problems — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  90. Augmenting Operations Research with Auto-Formulation of Optimization Models From Problem Descriptions — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
  91. Automatic Generation of Socratic Subquestions for Teaching Math Word Problems — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  92. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models — 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj
  93. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models — 2022 · arXiv:2201.11903
  94. Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning — 2022 · arXiv:2209.14610
  95. Generating Sequences by Learning to Self-Correct — 2022 · arXiv:2211.00053
  96. HAWP: a Dataset for Hindi Arithmetic Word Problem Solving — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
  97. Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
  98. Is a Question Decomposition Unit All We Need? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  99. LILA: A Unified Benchmark for Mathematical Reasoning — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
  100. Language models show human-like content effects on reasoning tasks — 2022 · arXiv:2207.07051

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